Evaluation of Potentially Avoidable Acute Care Utilization Among Patients Insured by Medicare Advantage vs Traditional Medicare

Key Points Question How do rates of hospitalizations, emergency department (ED) direct discharges, and observation stays associated with ambulatory care−sensitive conditions compare between Medicare Advantage and traditional Medicare? Findings This cross-sectional study of more than 10 million beneficiaries found that patients who experienced an ambulatory care−sensitive condition and were covered by Medicare Advantage were less likely to be hospitalized and more likely to be discharged directly from the ED or have an observation stay than were patients with traditional Medicare. Meaning The findings of this cross-sectional study suggest that by shifting to other care settings, including ED direct discharges and observation stays, Medicare Advantage may be avoiding hospitalizations (acute care visits) for ambulatory care−sensitive conditions.

eMethods 1. Supplemental methods eTable 1. Patient characteristics for Medicare Advantage contracts with highly reliable data versus Medicare Advantage excluded contracts Table 2. Event characteristics for Medicare Advantage contracts with highly reliable data versus Medicare Advantage excluded contracts eTable 3. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using linear regression models eTable 4. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using 11% HCC deflation eTable 5. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among MA HMOs versus MA PPOs using linear regression models eTable 6. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among MA high-quality versus MA lower-quality plans eTable 7. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using Poisson models, limiting to counties with 100+ patients eTable 8. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using Poisson models, accounting for overdispersion eTable 9. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using Poisson models, accounting for overdispersion and with months alive as offset eTable 10. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using Poisson models, accounting for overdispersion and without HCC adjustment eTable 11. Differences in avoidable acute care episodes related to ambulatory care-sensitive conditions among Medicare Advantage versus Traditional Medicare using Poisson models, stratified by geographic region eTable 12. Discharge location after ED direct discharge or observation stay for Medicare Advantage versus Traditional Medicare eMethods 1. Supplemental methodology

Identification of high-reliable MA contracts
The Medicare Payment Advisory Commission (MedPAC) has noted concerns about the completeness of MA encounter data from years prior to 2018. Therefore, we have followed a recently validated approach that identifies MA contracts with high reliability of completeness as performed by Jung et al. 18 This approach identifies highly reliable contacts by cross-checking MA encounter data with other external data sources, including the CMS Medicare Provider Analysis and Review (MedPAR) file and the Healthcare Effectiveness Data and Information Set (HEDIS) on the number of inpatient stays, ambulatory care visits, and ED visits. The list of highly reliable contracts were made available by Jung et al. In total, there were 210 contracts in 2018 MA Encounter data that were included in our analyses while 508 MA contracts were excluded. On average, there were 13,426 MA beneficiaries in each contract with highly reliable data vs. 3,427 MA beneficiaries in less reliable contracts. In eTable 1, we compare the characteristics of MA beneficiaries in highly reliable MA contracts vs. those in low reliable contracts. In eTable 2, we also compare the number of acute care visits across each type of episode in the highly reliable MA contracts vs. those in less reliable MA contracts.

Calculating risk scores
When calculating HCC scores, given potential concerns for upcoding, we limited our calculation of HCC risk scores to inpatient and outpatient files only for both MA and TM beneficiaries. We excluded records made from home health nursing visits and the carrier file as well, given potential concerns that these codes contribute to aggressive upcoding in MA.

Socioeconomic status
We used the Area Deprivation Index (ADI), a widely used measure of socioeconomic neighborhood disadvantage, based on the beneficiaries' zip code of residence. The ADI variable was downloaded from the Neighborhood Atlas website created by the University of Wisconsin School of Medicine and Public Health at the following link: https://www.neighborhoodatlas.medicine.wisc.edu/.

Star Ratings Analysis
For the sensitivity analysis examining adjusted differences in the number of episodes among MA patients in high-quality (i.e., 4-5 star) plans vs. patients in lower-quality (i.e, 1-3 star) plans, we identified star ratings using 2020 star ratings, which correspond to 2018 MA encounter data. 37 These models used Hospital Referral Region (HRR)-fixed effects instead of county-fixed effects to address sparsity of certain types of plans in some counties.  2) Differences reflect estimates from linear regression models. HCC scores were deflated by 6% in all models for all MA beneficiaries to account for more aggressive coding practices. Analyses have been adjusted for age, sex, dual status for Medicaid eligibility, self-reported race and ethnicity, the number of months alive for each beneficiary, HCC risk score, and include county-fixed effects.
3) For the 12 primary outcomes in this table, we used a Bonferroni adjustment, assessing for P-values less than 0.004. All coefficients met this threshold for statistical significance. 1) The sample for this analysis included beneficiaries in MA High-Quality Plans (N=1,878,048) and MA Lower-Quality Plans (N=520,015).
2) Relative risks (RR) reflect estimates from Poisson models. HCC scores were deflated by 6% in all models for all MA beneficiaries to account for more aggressive coding practices. Analyses have been adjusted for age, sex, dual status for Medicaid eligibility, self-reported race and ethnicity, the number of months alive for each beneficiary, HCC risk score, and include Hospital Referral Region-fixed effects.
3) For the 12 primary outcomes in this table, we used a Bonferroni adjustment, assessing for P-values less than 0.004. All coefficients met this threshold for statistical significance besides hospitalizations for acute conditions and observation stays for acute conditions. 2) Relative risks (RR) reflect estimates from Poisson models, accounting for overdispersion. HCC scores were deflated by 6% in all models for all MA beneficiaries to account for more aggressive coding practices. Analyses have been adjusted for age, sex, dual status for Medicaid eligibility, self-reported race and ethnicity, the number of months alive for each beneficiary, HCC risk score, and include county-fixed effects.
3) For the 12 primary outcomes in this 1) The sample for this analysis included Medicare Advantage (N=2,665,340) and Fee-for-Service (N=7,981,576) beneficiaries.
2) Relative risks (RR) reflect estimates from Poisson models, accounting for overdispersion. HCC scores were deflated by 6% in all models for all MA beneficiaries to account for more aggressive coding practices. Analyses have been adjusted for age, sex, dual status for Medicaid eligibility, self-reported race and ethnicity, the number of months alive for each beneficiary, HCC risk score, and include county-fixed effects.
3) For the 12 primary outcomes in this table, we used a Bonferroni adjustment, assessing for P-values less than 0.004. All coefficients met this threshold for statistical significance besides hospitalizations for chronic conditions.